COMPUTO

A new born academic journal promoting reproducibility
Supported by the French Statistical Society

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Comité éditorial

Julien Chiquet (Chief Editor)

Statistical learning for life science
Université Paris-Saclay, AgroParisTech, INRAE

Pierre Neuvial

Statistique,
CNRS, Institut de Mathématiques de Toulouse

Nelle Varoquaux

Machine learning and causal inference for genomics
CNRS, Université Grenoble Alpes

Chloé-Agathe Azencott

Machine Learning for therapeutic research
Mines ParisTech, Inserm, Institut Curie

Mathurin Massias

Optimisation
CR INRIA, Lyon

François-David Colin

IR
IR CNRS, IMAG, Montpellier

Facts in Stats/ML academic publications

  • Multiplication of academic journals and articles
    → at the expense of quality / scientific accuracy?
  • Lack of valorisation of computer/algorithmic developments and case studies
  • Saturation of existing solutions
  • Rapid evolution of tools
    • Great API (Rstudio, VScode)
    • Scientific Publishing system (quarto, Jupyter)
    • Integration / dynamics (git(hub), Actions)

Computo’s Board Positioning

Need to renew the mode of publication of scientific knowledge and know-how

Existing solutions

Standard academic journals

Statistics and Computing, Computational Statistics and Data Analysis, Journal of Computational and Graphical Statistics, JMLR, JRSS-B, JASA, …

Limitations

Fixed format (non-dynamic typically, PDF) limiting reproducibility

Software journals

R journal, Journal of Statistical Software, Journal of Open Source Software, JMLR Machine Learning Open Source Software, ROpen-Sci…

Limitations

Congestion, language-centric, software documentation, not structured around a scientific question

Computo in a nutshell

Aims and Scope

Promote contributions in statistics and machine learning that provide insight into which models/methods are the most appropriate to a specific question.

Open and reproducible

  • reproducibility of numerical results is a necessary condition for publication
  • all necessary data and code must be available.
  • reviews are open (reviewers can remain anonymous)

Assessing reproducibility

At the submission stage!

How does Computo work? (1/3)

How does Computo work? (2/3)

1. Advanced notebook System

https://quarto.org (embed Jupyter and RMarkdown)

  • Code (Python/Julia/R)
  • Math (\(\LaTeX\))
  • Biblio (\(bibTeX\))
  • Interactivity (HTML widget, CSS)

2. Git repository and services

github/github-action

  • Continuous Integration (reproducibility script)
  • Projects Management (submission, publication)
  • Issues (reviewing, discussion)

How does Computo work? (3/3)

3. Container service

binder

  • Easy to customize
  • Easy to interface with github

4. Reviewing system

Scholastica

  • Discussion among Editorial board
  • Reviewer Invitation
  • Anonymous exchanges between authors/reviewers

→ Eventually published (as Issues)

Author point of view (1/4)

Step 0: setup a github repository

Copy our template repository to use it as a starter

Author point of view (2/4)

Step 1. write your contribution

Write your notebook as usual (Same spirit as Jupyter/Rmarkdown).

Step 2: configure your conda/binder environment

file environment.yml

name: computorbuild
channels:
  - conda-forge
dependencies:
  - jupyter
  - numpy
  - r-base=4.2.0

Author point of view (3/4)

Step 3: proof reproducibility

A git push will trigger build process on github

name: build
on: push
jobs:
  computorticle:
    runs-on: ubuntu-latest
    steps: # [...]
      - name: Installing dependencies with Miniconda
        uses: conda-incubator/setup-miniconda@v2
          environment-file: environment.yml
        # [...]
      - name: Rendering with Quarto
        run: quarto render content.qmd
        # [...]
      - name: Deploying article on github pages
        # [...]

Author point of view (4/4)

Step 4. submit

If the build process is successful,

  • An HTML version is pushed on a github-page
  • A PDF version can be obtained via chrome-print
  • a binder repos can be associated

→ Submit the PDF on Scholastica page

See our quarto template for more

Editor point of view

Once the reviewing process has ended (successfully!)

  • Copy the author’s repository
  • Format to the final version
  • Publish reviews as issues
  • Add entry on the Journal web site referring
    • github repository
    • data repository
    • reviewing

See our mock contribution for more

First published paper

https://github.com/computorg/published-202204-deeplearning-occupancy-lynx

Computo’s first year review

  • framework functional + doi, ISSN, etc.
  • 1 published article, 3 articles under review, 2 or 3 promises of submissions
  • 4 talks in reproducibility workshops (Montpellier, Toronto, Humastica, IHP)
  • difficulty to find reviewers
  • neither technical nor human support

How could you help?

Authoring

Submit your work!

Reviewing

Volunteer by filling this form: https://forms.gle/P9iYJANuNM4WTVHDA

Feeding-in Computo with Peer-community-in

Get involved in a stat/ML PCI to feed Computo

  • Existing skeleton: CompStat PCI https://compstat.peercommunityin.org/
  • Identified scientific leader: Christian Robert
  • Technical/Web support needed
  • Active community of recommenders needed

https://computo.sfds.asso.fr